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Purpose: Tumor-only genomic testing can uncover somatic and germline pathogenic variants [pathogenic/likely pathogenic (P/LP)] in cancer predisposition genes. We describe the prevalence of P/LPs in BRCA1/2 and PALB2 (B1B2P2) across malignancies and the frequency of clinical germline testing (CGT) in patients with P/LPs in B1B2P2 identified on tumor-only testing. Experimental Design: Among 7,575 patients with cancer tested between 2016 and 2018 with the OncoPanel tumor-only sequencing assay, we characterized P/LP frequencies by tumor type, receipt of CGT prior to or within 12 months after OncoPanel, and factors associated with CGT. Results: 272 (3.6%) patients had OncoPanel-detected P/LPs in B1B2P2: 37.5% of P/LPs were in BRCA-related cancers; the remainder were in non-BRCA tumors. P/LPs were detected in ≥5% of breast, pancreatic, prostate, ovarian, nonmelanoma skin, endometrial, small cell lung, and colorectal cancers. 37.9% of patients with P/LPs received CGT prior to OncoPanel; an additional 10.7% underwent CGT within 12 months of OncoPanel. Among 132 with CGT, 88.6% had ≥1 clinical factor for CGT compared with 47.1% who did not undergo CGT. Patients with BRCA tumors were more likely to have CGT compared with those without (81.4% vs. 29.0%, P < 0.0001). Among patients with CGT, 70.5% (93/132) of P/LPs were germline. Conclusions: Tumor-only genomic testing identified P/LPs in B1B2P2 in 3.6% of patients. 52.9% of patients with tumor-detected P/LPs and without CGT did not meet personal or family history criteria for CGT. In addition, some patients with tumor-detected P/LPs were not referred for CGT, especially those with non-BRCA tumors. Given implications for treatment selection and familial cancer risk, processes to reliably trigger CGT from tumor-genomic findings are needed.

Gleason score, a measure of prostate tumor differentiation, is the strongest predictor of lethal prostate cancer at the time of diagnosis. Metabolomic profiling of tumor and of patient serum could identify biomarkers of aggressive disease and lead to the development of a less-invasive assay to perform active surveillance monitoring. Metabolomic profiling of prostate tissue and serum samples was performed. Metabolite levels and metabolite sets were compared across Gleason scores. Machine learning algorithms were trained and tuned to predict transformation or differentiation status from metabolite data. A total of 135 metabolites were significantly different (Padjusted < 0.05) in tumor versus normal tissue, and pathway analysis identified one sugar metabolism pathway (Padjusted = 0.03). Machine learning identified profiles that predicted tumor versus normal tissue (AUC of 0.82 ± 0.08). In tumor tissue, 25 metabolites were associated with Gleason score (unadjusted P < 0.05), 4 increased in high grade while the remainder were enriched in low grade. While pyroglutamine and 1,5-anhydroglucitol were correlated (0.73 and 0.72, respectively) between tissue and serum from the same patient, no metabolites were consistently associated with Gleason score in serum. Previously reported as well as novel metabolites with differing abundance were identified across tumor tissue. However, a “metabolite signature” for Gleason score was not obtained. This may be due to study design and analytic challenges that future studies should consider. Implications: Metabolic profiling can distinguish benign and neoplastic tissues. A novel unsupervised machine learning method can be utilized to achieve this distinction.

Purpose: DNA damage response and repair (DDR) gene alterations are associated with increased tumor-infiltrating lymphocytes, higher genomic instability, and higher tumor mutational burden (TMB) in cancer. Whether DDR alterations are associated with clinical outcomes to programmed death ligand 1 [PD-(L)1] blockade in non–small cell lung cancer (NSCLC) is unknown. Experimental Design: Tumors from patients treated with PD-(L)1 inhibitors were analyzed using targeted next-generation sequencing (NGS). Cancers were categorized on the basis of the presence or absence of deleterious mutations across a panel of 53 DDR genes. Clinical outcomes to PD-(L)1 inhibitors were evaluated according to DDR mutation status. Results: Of 266 patients with successful NGS who received PD-(L)1 inhibitors, 132 (49.6%) were identified as having deleterious DDR mutations (DDR-positive). DDR-positive and DDR-negative groups were similar in terms of baseline clinicopathologic characteristics. The median TMB was significantly higher in the DDR-positive group compared with the DDR-negative group (12.1 vs. 7.6 mutations/megabase; P < 0.001). Compared with DDR-negative patients (N = 134), DDR-positive patients had a significantly higher objective response rate (30.3% vs. 17.2%; P = 0.01), longer median progression-free survival [PFS; 5.4 vs. 2.2 months; HR, 0.58 (95% confidence interval (CI), 0.45–0.76); P < 0.001], and longer median overall survival [OS; 18.8 vs. 9.9 months; HR, 0.57 (95% CI, 0.42–0.77); P < 0.001] with PD-(L)1 therapy. After adjusting for PD-L1, TMB, performance status, tobacco use, and line of therapy, DDR-positive status was associated with a significantly longer PFS [HR, 0.68 (95% CI, 0.51–0.92); P = 0.01] and OS [HR, 0.60 (95% CI, 0.43–0.85); P = 0.004] in multivariate analysis. Conclusions: Deleterious DDR mutations are frequent in NSCLC and are associated with improved clinical outcomes in patients with NSCLC treated with PD-(L)1 blockade.

Programmed cell death ligand 1 (PD-L1) tumor proportion score (TPS) is the primary clinically-available biomarker of response to immunotherapy in non-small-cell lung cancer (NSCLC), but factors associated with PD-L1 expression are not well understood.Consecutive nonsquamous NSCLCs with successful PD-L1 assessment and targeted next-generation sequencing were included in this retrospective study. Clinicopathological characteristics, gene mutations, and copy number changes in gene and chromosomal arms were compared among three PD-L1 expression groups: negative (TPS1%), low (TPS 1%-49%), and high (TPS ≥ 50%). A Q-value0.25 was considered significant after multiple comparisons correction.A total of 909 nonsquamous NSCLCs were included. High PD-L1 expression compared with low and negative PD-L1 expression was associated with increased tobacco exposure (median pack-years: 25 versus 20 versus 20, respectively; P = 0.01), advanced stage at diagnosis (76% versus 67% versus 61% with advanced stage of disease, respectively; P0.001), and higher tumor mutational burden (TMB) (median 12.2 versus 10.6 versus 10.6 mutations/megabase, respectively; P0.001). Negative PD-L1 expression when compared with high PD-L1 expression was associated with: mutations in STK11 (19% versus 5%; Q0.001), EGFR (22% versus 11%; Q0.001), CTNNB1 (4.3% versus 0.4%; Q = 0.04), APC (5% versus 1%; Q = 0.17), and SMARCA4 (9% versus 4%; Q = 0.20); copy number loss of CD274 (PD-L1, 28% versus 6%; Q0.001), PDCD1LG2 (PD-L2, 28% versus 6%; Q0.001), and JAK2 genes (27% versus 7%; Q0.001), loss of chromosomal arm 9p (23% versus 10%; Q = 0.04), and gain of 1q (46% versus 21%; Q 0.001). High PD-L1 expression compared with negative PD-L1 expression was associated with copy number gain of CD274 (11% versus 3%; Q = 0.01) and PDCD1LG2 (11% versus 3%; Q = 0.01). NSCLCs with CD274 loss, compared with those without loss, had a lower response rate (23% versus 9%; P = 0.006) and shorter progression-free survival (3.3 versus 2.0 months; P = 0.002) on immunotherapy.PD-L1 expression is associated with specific genomic alterations and clinicopathologic characteristics in nonsquamous NSCLC.

Purpose:Molecular mechanisms of acquired resistance to MET tyrosine kinase inhibitors (TKI) are poorly understood. We aimed to characterize the genomic mechanisms of resistance to type I and type II MET TKIs and their impact on sequential MET TKI therapy outcomes in patients with metastatic MET exon 14–mutant NSCLC.Experimental Design:Genomic alterations occurring at the time of progression on MET TKIs were studied using plasma and tissue next-generation sequencing (NGS).Results:A total of 20 patients had tissue or plasma available for analysis at the time of acquired resistance to a MET TKI. Genomic alterations known or suspected to be mechanisms of resistance were detected in 15 patients (75%). On-target acquired mechanisms of resistance, including single and polyclonal MET kinase domain mutations in codons H1094, G1163, L1195, D1228, Y1230, and high levels of amplification of the MET exon 14–mutant allele, were observed in 7 patients (35%). A number of off-target mechanisms of resistance were detected in 9 patients (45%), including KRAS mutations and amplifications in KRAS, EGFR, HER3, and BRAF; one case displayed both on- and off-target mechanisms of resistance. In 2 patients with on-target resistant mutations, switching between type I and type II MET TKIs resulted in second partial responses.Conclusions:On-target secondary mutations and activation of bypass signaling drive resistance to MET TKIs. A deeper understanding of these molecular mechanisms can support the development of sequential or combinatorial therapeutic strategies to overcome resistance.

PURPOSE Given regulatory approval of immune checkpoint inhibitors in patients with mismatch repair–deficient (MMR-D) cancers agnostic to tumor type, it has become important to characterize occurrence of MMR-D and develop cost-effective screening approaches. Using a next-generation sequencing (NGS) panel (OncoPanel), we developed an algorithm to identify MMR-D frequency in tumor samples and applied it in a clinical setting with pathologist review. METHODS To predict MMR-D, we adapted methods described previously for use in NGS panels, which assess patterns of single base-pair insertion or deletion events occurring in homopolymer regions. Tumors assayed with OncoPanel between July 2013 and July 2018 were included. For tumors tested after June 2017, sequencing results were presented to pathologists in real time for clinical MMR determination, in the context of tumor mutation burden, other mutational signatures, and clinical data. RESULTS Of 20,301 tumors sequenced, 2.7% (553) were retrospectively classified as MMR-D by the algorithm. Of 4,404 samples with pathologist sign-out of MMR status, the algorithm classified 147 (3.3%) as MMR-D: in 116 cases, MMR-D was confirmed by a pathologist, five cases were overruled by the pathologist, and 26 were assessed as indeterminate. Overall, the highest frequencies of OncoPanel-inferred MMR-D were in endometrial (21%; 152/723), colorectal (9.7%; 169/1,744), and small bowel (9.3%; 9/97) cancers. When algorithm predictions were compared with historical MMR immunohistochemistry or polymerase chain reaction results in a set of 325 tumors sequenced before initiation of pathologist assessment, the overall sensitivity and specificity of the algorithm were 91.1% and 98.2%, respectively. CONCLUSION We show that targeted, tumor-only NGS can be leveraged to determine MMR signatures across tumor types, suggesting that broader biomarker screening approaches may have clinical value.

Imaging datasets in cancer research are growing exponentially in both quantity and information density. These massive datasets may enable derivation of insights for cancer research and clinical care, but only if researchers are equipped with the tools to leverage advanced computational analysis approaches such as machine learning and artificial intelligence. In this work, we highlight three themes to guide development of such computational tools: scalability, standardization, and ease of use. We then apply these principles to develop PathML, a general-purpose research toolkit for computational pathology. We describe the design of the PathML framework and demonstrate applications in diverse use cases. PathML is publicly available at www.pathml.com.

Purpose: The purpose of this study is to characterize the mutational landscape across the spectrum of urothelial carcinoma (UC) to identify mutational features and potential therapeutic targets. Experimental Design: Using targeted exome sequencing (n = 237 genes), we analyzed the mutation spectra of 82 low-grade nonmuscle-invasive bladder cancers (LG-NMIBC), 126 high-grade (HG) NMIBC, 199 muscle-invasive bladder cancers (MIBC), 10 LG-upper tract urothelial cancers (LG-UTUC), and 55 HG-UTUC. Results: FGFR3 and KDM6A mutations were significantly more common in LG-NMIBC (72% and 44%, respectively) versus other bladder subtypes. FGFR3 alterations were also enriched in LG-UTUC versus HG-UTUC tumors (80% vs. 16%). In contrast, TP53 and RB1 mutations were significantly more frequent in all 3 HG urothelial carcinoma subtypes than in LG-NIMBC (45%–58% vs. 4%; 9%–22% vs. 0; respectively). Among LG-NMIBC tumors, KDM6A mutations were more common in women than in men (71% vs. 38%). HG-NMIBC and MIBC had higher tumor mutational burden (TMB) than LG-NMIBC (P = 0.001 and P < 0.01, respectively). DNA-damage repair (DDR) alterations were associated with a higher TMB in HG-NMIBC and MIBC tumors, and these two tumor types were also enriched for an APOBEC mutational signature compared with LG-NMIBC and HG-UTUC. Alterations in FGFR3, PIK3CA, and EP300 correlated with worse overall survival in HG-UTUC and occurred concurrently. Conclusions: Our analysis suggests that a fraction of MIBCs likely arise from precursor lesions other than LG-NMIBC. KDM6A mutations are twice as common in women with LG-NIMBC than those in men. DDR gene mutations and APOBEC mutagenesis drive mutations in HG-NMIBC and MIBC. UTUC has a distinct mutation profile from bladder cancer.

Purpose: To date no biomarker has been identified that predicts response to ICI in mTNBC. This study aimed to explore if tumor genomic alterations correlate with efficacy of PD-1/PD-L1 inhibition in patients (pts) with mTNBC. Methods: Demographic, treatment response, and long-term outcome data were collected on patients with mTNBC treated at Dana-Farber Cancer Institute (DFCI) under several clinical trials incorporating PD-1/PD-L1 inhibitors, given as monotherapy or combined with chemotherapy (CT). Pts included in this analysis had available results of targeted exon sequencing performed using Oncopanel, our institutional gene sequencing panel, on archival tumor tissue. TMB was calculated by determining the number of non-synonymous somatic mutations that occur per megabase of exonic sequence data across all genes on the panel. High TMB was defined as 310 mutations/megabase. TMB and gene alterations were correlated with objective response rate (ORR) per RECIST 1.1, progression-free (PFS) and overall survival (OS). Results: A total of 50 pts with mTNBC were included in this analysis. At baseline, the median age was 55.9 years (31.8–75.9), 60% had ECOG 0 and 40% had ECOG 1, 72% had visceral metastasis, and 46% had received 31 prior lines of systemic therapy in the metastatic setting. While 26% of pts received monotherapy [pembrolizumab (n=7, NCT02447003); atezolizumab (n=6; NCT01375842)], 74% received combination with CT [pembrolizumab plus eribulin (n=31; NCT02513472); atezolizumab plus nab-paclitaxel (n=6; NCT01633970)]. PTEN alterations were present in 30% of pts (mutations = 7; one copy number loss = 7; two copy number loss = 1). Median follow-up was 14 months (1–40). Pts with tumors harboring PTEN alterations had lower ORR (7% vs 57%; P Table 1.Multivariable analysis for PFS Hazard ratioConfidence Intervalp-valueCombination therapy0.420.16 – 1.130.009Visceral metastasis1.310.63 – 2.770.46Previous lines of therapy1.020.09 – 0.700.85ECOG 12.11.06 – 1.280.034PTEN altered3.741.65 – 8.440.002Hypermutated tumors0.850.75 – 0.970.011 Citation Format: Barroso-Sousa R, Tyekucheva S, Pernas-Simon S, Exman P, Jain E, Garrido-Castro AC, Hughes M, Bychkovsky B, Di Lascio S, Umeton R, Files J, Lindeman NI, MacConaill LE, Hodi FS, Krop IE, Dillon D, Winer EP, Wagle N, Lin NU, Mittendorf EA, Tolaney SM. PTEN alterations and tumor mutational burden (TMB) as potential predictors of resistance or response to immune checkpoint inhibitors (ICI) in metastatic triple-negative breast cancer (mTNBC) [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P5-12-02.

Prostate cancer (PCa) is associated with several genetic alterations which play an important role in the disease heterogeneity and clinical outcome. These alterations involve gene fusion between TMPRSS2 and members of the ETS family of transcription factors like ERG, ETV1, and ETV4 together with mutations or deletions in tumor suppressors like TP53 and PTEN. The expanding wealth of digital whole slide images (WSIs) and the increasing adoption of deep learning approaches offer a unique opportunity for pathologists to streamline the detection of these alterations. Here, we used 736 haematoxylin and eosin-stained WSIs from 494 primary PCa patients to identify several key genetic alterations including ERG, ETV1, and ETV4 fusion, PTEN loss, and TP53 and SPOP mutations. Using a custom segmentation pipeline, we identified tissue regions and tiled them into high-resolution (10X magnification) patches (256X256 pixels) which were passed to our deep multiple instance learning framework. Using a pre-trained ResNet50 model, we extracted informative features which were subsequently used for training to predict slide-level labels and to detect slide regions with high diagnostic relevance. Using a 10-folds cross validation approach, we divided the data into training (80%), validation (10%) and testing (10%) sets. The training and validation data were used for training the model and hyperparameters tuning, respectively while the testing data was used to provide an unbiased evaluation of the models’ performance using the mean Area Under the Receiver Operating Characteristic (AUROC) across the ten testing folds as evaluation metric. We managed to accurately detect key molecular alterations including ERG fusion, ETV1 fusion, ETV4 fusion, and PTEN loss. Additionally, we were able to detect mutations in TP53 and SPOP together with the presence of androgen-receptor splice variant 7 (ARv7). In addition to slide-level classification, we also identified subregions with high attention score which can help pathologists identify the distinct morphological features associated with each genetic alteration. Finally, in order to examine the cellular structure associated with each genetic alteration, we used Hover-Net model to segment and classify the nuclei in the high-attention tiles. Our work highlights the utility of using WSIs to accurately identify key molecular alteration in cancer and their associated morphological and cellular features on the slide which would streamline the diagnostic process. To the best of our knowledge, this is the first study that uses routine WSIs to predict and characterize key genetic alterations in PCa. Citation Format: Mohamed Omar, Zhuoran Xu, Ryan Carelli, Jacob Rosenthal, David Brundage, Daniela C. Salles, Eddie L. Imada, Renato Umeton, Edward M. Schaeffer, Brian D. Robinson, Tamara L. Lotan, Massimo Loda, Luigi Marchionni. Using attention-based deep multiple instance learning to identify key genetic alterations in prostate cancer from whole slide images [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 462.

Background: Effective biomarkers are urgently needed in the clinical settings. However, most biomarkers are currently developed from single type of omics data. The goal of this study is to identify prognostic prostate cancer signatures using transcriptomics and metabolomics profiles jointly aiming to capture wider spectrum of biological information. Methods: In this study, we included 94 tumor and 48 adjacency normal samples with both transcriptomics and metabolomics profiles from Dana-Farber/Harvard Cancer Center SPORE Prostate Cancer Cohort. There were 85 patients being followed up with median length of 2.02 years including 3 lethal and 8 progression cases. We first constructed a multi-omics covering network that contained minimal set of variable pairs but sufficiently rich to account for observed inter-patient variations. The network was built on known gene-metabolite interaction pairs from Pathway Commons as prior knowledge. Next, we used a diffusion process with each of connected gene-metabolite pairs as seeds to identify candidate signatures in the network. Signature sizes were controlled under 5 by adjusting the tree height during pruning. Hierarchical clustering and survival analyses were then performed to stratify patients into two risk groups for disease-free survival probability. Only the prognostic signatures with power higher than 0.9 from bootstrapping were kept for external validation and functional analyses. Since to our best knowledge, there is no publicly available dataset with both transcriptomics and metabolomics to validate the multi-omics signature we identified. We thus trained a rank-based kTSP classifier using gene expression data in SPORE cohort as a surrogate signature and validated it in TCGA prostate cancer cohort independently. Results: Constructed covering network consisted of 12 metabolites and 54 genes with inter-patient heterogeneities being captured efficiently. We identified one high-powered multi-omics signature (Gene: EGLN3; Metabolites: succinate, trans-4-hydroxyproline) that exhibited good prognostic value where high-risk patients had significant less time of disease-free survival (log rank test: p=0.019, power: 0.974). In addition, genomic variations were observed in different percentages of patients in high and low risk groups including NCOR1(SNV, 70.6% vs 96.3% p=0.025), NKX3.1(CNV, 29.6% vs 64.7% p=0.031) and TNFRSF10C (CNV, 29.6% vs 64.7%, p=0.031). No evidence indicated the patient grouping by the signature depend on Gleason scores (p=0.44). The surrogated gene signature contained 6 pairs of genes that can effectively classify TCGA patients into two prognostic groups (log rank test: p=0.048). Still, no evidence indicated the surrogate gene signature is associated with Gleason score (p=0.23) in TCGA dataset. Conclusions: We identified a prognostic multi-omics signature (EGLN3, succinate, trans-4-hydroxyproline) with high statistical power. Citation Format: Zhuoran Xu, Elisa Benedetti, Ryan Carelli, Jacob Rosenthal, Hubert Pakula, Mohamed Omar, Renato Umeton, David Brundage, Jan Krumsiek, Massimo Loda, Luigi Marchionni. A multi-omics signature for patients’ risk classification in prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5858.

Pancreatic cancer is a leading cause of cancer-related deaths worldwide with increasing incidence. Early diagnosis of pancreatic cancer is a key challenge, as the disease is typically detected at a late stage. However, patients who present with early-stage disease can be cured by a combination of surgery, chemotherapy and radiotherapy. Thus, a better understanding of the risk factors for pancreatic cancer and detection at early stages has great potential to improve patient survival and reduce overall mortality from this aggressive malignancy. Here we exploit the power of advanced machine learning (ML) technology by focusing on the time sequence of clinical events and by predicting the risk of cancer occurrence over a multi-year time interval. This investigation was initially carried out using the Danish National Patient Registry (DNPR) and data which covers 41 years (1977 to 2018) of clinical records for 8.6 million patients, of which about 40,000 had a diagnosis of pancreatic cancer. To maximize predictive information extraction from these records we tested a range of ML methods, ranging from regression methods and ML without or with time dependence to time series methods such as GRU and Transformer. We explicitly train machine learning models on the time sequence of diseases in patient clinical histories and test the ability to predict cancer occurrence in time intervals of 3 to 60 months after risk assessment. For cancer occurrence within 36 months, the performance of the best model (AUROC=0.88; OR=47.5 for 20% recall and OR=159.0 for 10% recall), substantially exceeds that of a model without time information, even when disease events within a 3 month window before cancer diagnosis are excluded from training (AUROC[3m]=0.84). Independent training and testing on the Boston dataset reaches comparable performance (AUROC=0.87, OR=112.0 for 20% recall and OR=162.4 for 10% recall). We also extract from the AI machine an estimate of the contribution to prediction of individual disease features, e.g., obesity and diabetes. These results raise the state-of-the-art level of performance of cancer risk prediction on real-world data sets and provide support for the design of future screening trials for high-risk patients. AI on real-world clinical records has the potential to shift focus from treatment of late-stage to early-stage cancer, benefiting patients by improving lifespan and quality of life. We expect further increases in prediction accuracy with the availability of data beyond disease codes, such as prescriptions, laboratory values, and images. Citation Format: Davide Placido, Bo Yuan, Jessica X. Hjaltelin, Amalie D. Haue, Piotr J. Chmura, Chen Yuan, Jihye Kim, Renato Umeton, Gregory Antell, Alexander Chowdhury, Alexandra Franz, Lauren Brais, Elizabeth Andrews, Debora S. Marks, Aviv Regev, Peter Kraft, Brian M. Wolpin, Michael Rosenthal, Søren Brunak, Chris Sander. AI predicts risk of pancreatic cancer from disease trajectories using real-world electronic health records (EHRs) from Denmark and the USA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB550.

Meningiomas are the most common primary tumor of the central nervous system, with ~28,000 new diagnoses annually in the United States1. Currently, there are no approved systemic therapies for meningiomas that recur following local treatment: chemotherapy and hormonal agents have demonstrated minimal benefit in numerous clinical trials2–4. Meningioma comprises a heterogeneous group of neoplasms driven by mutations in a wide array of tumor suppressor genes and oncogenes5–17. Characterization of these mutations has revealed opportunities for rational therapy18–20. For example, a durable therapeutic response has been reported for a metastatic AKT1(E17K)-mutant meningioma treated with a pan-AKT inhibitor.21 Studies also suggest the potential for treating meningioma with immune checkpoint modulators22–24: programmed death receptor 1 ligand (PD-L1) is expressed in a subset of meningiomas and the tumor microenvironment is immunosuppressive22–28. Higher-grade meningiomas also harbor mutations predicted to generate neoantigens, which may foster susceptibility to immunotherapies29. Based on these data, we initiated a phase II study of nivolumab, a humanized IgG4 PD-1 blocking monoclonal antibody, in patients with higher-grade meningiomas that recurred following surgery and radiotherapy ({"type":"clinical-trial","attrs":{"text":"NCT02648997","term_id":"NCT02648997"}}NCT02648997). We report here a patient with an atypical meningioma that was not controlled by repeated surgery and radiation but which was highly response to nivolumab.

The momentum of gene therapy in Huntington's disease (HD) deserves biomarkers from easily accessible fluid. We planned a study to verify whether plasma miRNome may provide useful peripheral “reporter(s)” for the management of HD patients. We performed an exploratory microarray study of whole non-coding RNA profiles in plasma from nine patients with HD and 13 matched controls [eight healthy subjects (HS) and five psychiatric patients (PP) to minimize possible iatrogenic impact on the profile of non-coding RNAs]. We found an HD-specific signature: downregulation of hsa-miR-98 (fold change, −1.5, p = 0.0338 HD vs. HS, and fold change, 1.5, p = 0.0045 HD vs. PP) and upregulation of hsa-miR-323b-3p (fold change, 1.5, p = 0.0007 HD vs. HS, and fold change, 1.5, p = 0.0111 HD vs. PP). To validate this result in an independent cohort, we quantify by digital droplet PCR (ddPCR) the presence of the two microRNA in the plasma of 33 HD patients and 49 matched controls (25 HS and 24 PP patients). We were able to confirm that hsa-miR-323b-3p was upregulated in HD and premanifest HD vs. HS and PP: the median values (first–third quartile) were 4.1 (0.9–10.53) and 5.8 (1.9–10.70) vs. 0.69 (0.3–2.75) and 1.4 (0.78–2.70), respectively, p < 0.05. No significant difference was found for hsa-miR-98. To evaluate the biological plausibility of the hsa-miR-323b-3p as a component of the disease pathophysiology, we performed a bioinformatic analysis based on its targetome and the huntingtin (HTT) interactome. We found a statistically significant overconnectivity between the targetome of hsa-miR-323b-3p and the HTT interactome (p = 1.48e−08). Furthermore, there was a significant transcription regulation of the HTT interactome by the miR-323b-3p targetome (p = 0.02). The availability of handy, reproducible, and minimally invasive biomarkers coming from peripheral miRNome may be valuable to characterize the illness progression, to indicate new therapeutic targets, and to monitor the effect of disease-modifying treatments. Our data deserve further studies with larger sample size and longitudinal design.

Background High tumor mutational burden (TMB) and PD-L1 expression are associated with improved clinical outcomes in patients (pts) with non-small cell lung cancer (NSCLC) treated with immune checkpoint inhibitors (ICIs). However, how TMB performs as a predictive biomarker to ICIs in different PD-L1 expression subgroups is not well characterized. Methods We collected clinicopathologic and genomic data from NSCLCs which underwent targeted NGS and TMB assessment at DFCI. An unbiased recursive partitioning (URP) algorithm was used to investigate an optimal TMB cut-off with respect to objective response rate (ORR) in the subset of pts treated with ICIs. This TMB cut-off was then validated in the prospective POPLAR/OAK cohort. Results Among 3560 NSCLCs identified, median TMB was significantly higher among current smokers compared to former (P 25%, suggesting that TMB differentially impacts response to immunotherapy among PD-L1 high versus low NSCLCs (figure 5). Conclusions The impact of TMB may vary across PD-L1 expression subgroups. Rational integration of TMB and PD-L1 expression may identify NSCLCs with the greatest likelihood of response or resistance to ICIs. Ethics Approval Clinicopathologic data were collected from patients with advanced NSCLC who had consented to a correlative research study (DF/HCC protocol #02-180).

BACKGROUND: MET exon 14 alterations are actionable oncogenic drivers. Durable responses to MET inhibitors are observed in patients with advanced MET exon 14-altered lung cancers in prospective trials. In contrast, the activity of immunotherapy, PD-L1 expression and tumor mutational burden (TMB) of these tumors and are not well characterized. PATIENTS AND METHODS: Patients with MET exon 14-altered lung cancers of any stage treated at two academic institutions were identified. A review of clinicopathologic and molecular features, and an analysis of response to single-agent or combination immune checkpoint inhibition were conducted. PD-L1 immunohistochemistry was carried out and TMB was calculated by estimation from targeted next-generation sequencing panels. RESULTS: We identified 147 patients with MET exon 14-altered lung cancers. PD-L1 expression of 0%, 1%–49%, and ≥50% were 37%, 22%, and 41%, respectively, in 111 evaluable tumor samples. The median TMB of MET exon 14-altered lung cancers was lower than that of unselected non-small-cell lung cancers (NSCLCs) in both independently evaluated cohorts: 3.8 versus 5.7 mutations/megabase (P

PURPOSE Heterogeneity in tumor mutational burden (TMB) quantification across sequencing platforms limits the application and further study of this potential biomarker of response to immune checkpoint inhibitors (ICIs). We hypothesized that harmonization of TMB across platforms would enable integration of distinct clinical data sets to better characterize the association between TMB and ICI response. METHODS Cohorts of patients with non–small-cell lung cancer sequenced by 1 of 3 targeted panels or by whole-exome sequencing (WES) were compared (N = 7,297). TMB was calculated uniformly and compared across cohorts. TMB distributions were harmonized by applying a normal transformation followed by standardization to z scores. In subcohorts of patients treated with ICIs (Dana-Farber Cancer Institute n = 272; Memorial Sloan Kettering Cancer Center n = 227), the association between TMB and outcome was assessed. Durable clinical benefit (DCB) was defined as responsive/stable disease lasting ≥ 6 months. RESULTS TMB values were higher in the panel cohorts than in the WES cohort. Average mutation rates per gene were highly concordant across cohorts (Pearson’s correlation coefficients, 0.842-0.866). Subsetting the WES cohort by gene panels only partially reproduced the observed differences in TMB. Standardization of TMB into z scores harmonized TMB distributions and enabled integration of the ICI-treated subcohorts. Simulations indicated that cohorts > 900 are necessary for this approach. TMB did not associate with response in never-smokers or patients who harbor targetable driver alterations, although these analyses were underpowered. An increase of TMB thresholds increased DCB rate, but DCB rates within deciles varied. Receiver operating characteristic curves yielded an area under the curve of 0.614, with no natural inflection point. CONCLUSION The z score conversion harmonizes TMB values and enables integration of data sets derived from different sequencing panels. Clinical and biologic features may provide context to the clinical application of TMB and warrant additional study.

Background: Although high TMB correlates with improved outcomes to immune checkpoint inhibitors (ICI) in patients (pts) with non-small cell lung cancer (NSCLC), an optimal TMB cut-off to discriminate cancers most likely to respond to ICI has not been identified. Whether TMB impacts outcomes to ICI in different PD-L1 levels subgroups is also unclear. Methods: Unbiased recursive partitioning (URP) was used to identify an optimal TMB cut-off for objective response rate (ORR) in two independent cohorts (DFCI and MSKCC) of pts with NSCLC treated with ICI. TCGA was interrogated to find differences in tumor immune cell subsets according to the TMB cut-off identified. Multiplexed immunofluorescence (IP) was also performed on NSCLC samples. Results: In the DFCI (N=686) and MSKCC (N=672) cohorts, URP found an optimal cut-off of TMB for ORR at 19 mutations/megabase (mut/Mb), corresponding to the 90th percentile in each cohort. Median progression-free (PFS) and overall survival (OS) were significantly longer in NSCLCs with TMB ≥19 mut/Mb vs Conclusion: A very high TMB is associated with better outcomes to ICI and a distinct immunophenotype in NSCLC. Rational integration of TMB and PD-L1 expression may identify NSCLCs most likely to respond to ICI. CohortPD-L1 expressionPFS TMB ≥ vs Citation Format: Biagio Ricciuti, Kathryn C. Arbour, Navin R. Mahadevan, Joao V. Alessi, James Lindsay, Renato Umeton, Rileen Sinha, Amir Hoojghan, Natalie Vokes, Gonzalo Recondo, Giuseppe Lamberti, Andrew Polio1, Hira Rizvi, Giulia Leonardi, Andrew J. Plodkowski, Kristen Felt, Bijaya Sharma, Michael Y. Tolstorukov, Pasi A. Janne, Eliezer M. Van Allen, Lynette M. Sholl, Scott J. Rodig, Matthew D. Hellmann, Mark M. Awad. A very high tumor mutational burden (TMB) is associated with improved efficacy of PD-(L)1 inhibition across different PD-L1 expression subgroups and a distinct immunophenotype in NSCLC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 490.

// Evan L. Busch 1, 2, 3 , Jason L. Hornick 4 , Renato Umeton 5 , Adem Albayrak 5 , Neal I. Lindeman 4 , Laura E. MacConaill 4 , Elizabeth P. Garcia 4 , Matthew Ducar 4 and Timothy R. Rebbeck 2, 3 1 Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA 2 Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA 3 Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA 4 Department of Pathology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA 5 Department of Informatics, Dana-Farber Cancer Institute, Boston, MA 02215, USA Correspondence to: Evan L. Busch, email: nhebu@channing.harvard.edu Keywords: metastasis, carcinomas, epithelial-mesenchymal transition, mutation, detachment Received: June 27, 2017 Accepted: September 01, 2017 Published: September 21, 2017 ABSTRACT Metastases are involved in most cancer deaths. Evidence has suggested that cancer cell detachment from primary tumors might occur largely via the mechanism of epithelial-mesenchymal transition (EMT) activated by epigenetic events, but data addressing other possible triggers of detachment, particularly genetic mutations, have been limited. Using the Profile study of cancer genomics at Dana-Farber Cancer Institute, we examined somatic mutations in the EMT genes CDH1 in 5,106 primary carcinomas and CTNNB1 in 7,578 primary carcinomas across 13 anatomic sites: urinary bladder, breast, colon/rectum, endometrium, esophagus, kidney, lung, ovary, pancreas, prostate, skin (non-melanoma), stomach, and thyroid. For each gene and anatomic site, we calculated the prevalence of primary carcinomas with at least one mutation. Across all anatomic sites, 4% of carcinomas had at least one CDH1 mutation and 4% of carcinomas had at least one CTNNB1 mutation. By anatomic site, the observed prevalence of carcinomas with at least one mutation was less than 5% at 10 sites for CDH1 and 12 sites for CTNNB1 . Tumor stage data were available for a subset of breast, colorectal, lung, and prostate tumors. Among patients from this subset who were diagnosed with regional or distant disease, only 4% had a CDH1 mutation and 1% had a CTNNB1 mutation in the primary tumor. The low mutation prevalences, especially among those with diagnoses of regional or distant disease, suggest that somatic mutations in CDH1 and CTNNB1 are unlikely to explain a substantial proportion of cancer cell detachment from primary carcinomas originating at most anatomic sites.

Background Clinically-available biomarkers to identify the fraction of patients with small cell lung cancer (SCLC) who respond to immune-checkpoint inhibitors (ICIs) are lacking. High nonsynonymous tumor mutational burden (TMB), as assessed by whole exome sequencing, correlates with improved clinical outcomes for patients with SCLC treated with ICIs. Whether TMB as assessed by targeted next generation sequencing (NGS) is associated with improved efficacy of ICIs in patients with SCLC is currently unknown. Here we determined whether TMB by targeted NGS is associated with efficacy of ICIs in patients with SCLC. Methods We collected clinicopathologic data from patients with relapsed or refractory SCLC which underwent targeted NGS with TMB assessment by the Dana-Farber Cancer Institute OncoPanel platform. The relationship between TMB and clinical outcomes after treatment with ICIs was investigated. Results Among the 52 patients treated with ICIs, we found no significant difference in the objective response rate (ORR) between patients with a TMB above the 50th percentile (“TMB high”) and those with a TMB at or below the 50th percentile (“TMB low”). The median progression-free survival (mPFS) and median overall survival (mOS) were significantly longer in patients with a high TMB compared to those with a low TMB (mPFS: 3.3 versus 1.2 months, HR: 0.37 [95% CI: 0.20–0.69], P

PD-L1 expression is the primary clinical biomarker of immune checkpoint blockade (ICB) response in non-small cell lung cancer (NSCLC); patients with tumors that express higher levels of PD-L1 tend to have better responses. However, somatic genomic events correlating with PD-L1 expression are not fully defined. To investigate this, we utilized a DFCI cohort of 909 non-squamous NSCLC samples grouped into 3 categories of PD-L1 expression based on immunohistochemistry (IHC): Compared to PD-L1 high tumors, PD-L1 neg. cases were more likely to have mutations in STK11 (Permutation test, q=7e-4), EGFR (q=7e-4), CTNNB1 (q=0.04), APC (q=0.17), and SMARCA4 (q=0.2). CN losses that more significantly occurred in PD-L1 neg. tumors included deletions of PD-L1, PD-L2, and JAK2 (Fisher exact test, q We assessed the association of these somatic alterations with PD-L1 mRNA expression in an independent cohort of 529 TCGA primary lung tumors. STK11 mutation (Mann-Whitney test, q Next, we examined if somatic alterations associated with PD-L1 expression were also correlated with progression free (PFS) or overall survival (OS) using a partially-overlapping DFCI cohort of 468 advanced stage NSCLCs. STK11 mutation (Log-rank test, q=0.1), EGFR mutation (q We observed the previously identified (Schabath, et al 2016) mutual exclusivity between EGFR and STK11 mutations. We further show that they may indicate distinct subtypes of PD-L1 neg. tumors with STK11 mutant tumors exhibiting strikingly worse prognosis after ICB (med. PFS 52 vs 64 days, p In summary, we confirm the findings that STK11 and EGFR mutations are associated with reduced PD-L1 expression and worse prognosis after ICB (Rizvi, et al 2018) and identify CN loss of the PD-L1 locus as a potential novel biomarker of lower PD-L1 expression and worse ICB outcomes. In addition, we show a novel anticorrelation between Wnt pathway alterations and PD-L1 expression by IHC. Taken together, this work suggests a role of specific mutations and CNVs in affecting PD-L1 expression and response to ICB. Citation Format: Liam F. Spurr, Giuseppe Lamberti, Yvonne Y. Li, Biagio Ricciuti, Gonzalo Recondo, Renato Umeton, Lynette M. Sholl, Matthew L. Meyerson, Andrew D. Cherniack, Mark M. Awad. Genomic correlates of PD-L1 expression are associated with response to immunotherapy in non-small cell lung cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5668.

Introduction: Tumor mutational burden (TMB) is a possible predictive biomarker for response to immune checkpoint inhibitors (ICI). However, there is significant heterogeneity in the calculation of TMB and the thresholds that have been identified to associate with clinical benefit. We analyzed TMB and clinical outcomes across next generation sequencing (NGS) panels to address inter-panel heterogeneity and to identify consistent TMB thresholds. Methods: Non-small cell lung cancer sequencing data generated from three validated clinical platforms (OncoPanel n = 1155, MSK-IMPACT n = 1520, and Foundation Medicine n = 3377) and whole exome sequencing from The Cancer Genome Atlas (TCGA, n = 1144) were obtained. Tumor mutational burden (TMB) was calculated across cohorts as the number of nonsynonymous mutations per length of genome sequenced. The distribution of TMB was plotted for each cohort and compared to TCGA. For the subset of patients treated with ICI (OncoPanel n = 296; MSK-IMPACT,n = 227), durable clinical benefit (DCB) was defined as responsive/stable disease lasting ≥ 6 months and was associated with TMB. Results: There was high concordance across sequencing panels in the mean number of mutations per individual gene (OncoPanel vs TCGA R2 = 0.8914; Foundation vs TCGA R2 = 0.9025; MSK-IMPACT vs TCGA R2 = 0.8579). Absolute TMB values were higher in NGS panels compared to TCGA, likely due to enrichment for oncogenes and differences in germline filtration. Harmonization between NGS panels and WES was attempted by applying a linear transformation of panel TMB values to WES TMB values. Linear transformation constants were variable at the highest and lowest TMB values, and in low TMB subgroups such as EGFR mutants and never smokers. Consistent with prior studies, TMB was higher in patients with DCB compared to those with no durable benefit (NDB) (Oncopanel p-value=0.003; MSK-IMPACT p-value=0.006). Increasing TMB thresholds were associated with increase in DCB rate, likely due to enrichment of the highest TMB responders; DCB rates did not linearly associate with TMB at lower TMB values. Conclusion: There were significant differences in TMB quantification among NGS and TCGA panels, particularly at lower TMBs. Categorization, rather than fitting absolute values, may be an approach for harmonizing distinct panels. In these cohorts, the relationship between benefit of ICI and TMB was not consistent across the TMB spectrum but accentuated in the highest TMB patients. These findings may have implications for the application of TMB as a biomarker in clinical practice. Citation Format: Natalie Vokes, Elizabeth Jimenez Alguilar, Renato Umeton, Anika Adeni, Lynette Sholl, Matthew Hellmann, Hira Rizvi, Mark Awad, Eliezer Van Allen. Inter-test variability in tumor mutational burden (TMB) quantification and identification of TMB thresholds [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2514.

9082 Background: Immune checkpoint inhibitors (ICI) treatment can result in durable responses for KRAS-mutant (mut) non-small cell lung cancer (NSCLC). The impact of KRAS allele subtypes and concurrent genomic alterations on ICIs efficacy is unknown. Methods: We collected clinicopathologic and genomic data from patients (pts) with advanced NSCLC treated with programmed death (PD)-1 axis inhibition at the Dana-Farber Cancer Institute. We evaluated outcomes to ICIs according to KRAS mut alleles and concurrent STK11 and KEAP1 mut. Results: Of 617 ICI-treated NSCLCs, 181 (29.3%) had KRAS mut. Median TMB (mTMB) and median PD-L1 tumor proportion score (TPS) were similar between KRAS mut and KRAS wild type (wt) tumors. Among tumors with KRAS codon 12 mut, mTMB was higher in G12V (n = 37, 12.2 mut/Mb) compared to G12C (n = 84, 11.4 mut/Mb), G12D (n = 20, 9.4 mut/Mb) and G12A (n = 13, 10.1 mut/Mb), P = 0.05. Tumors with KRAS transversions (Tv) (n = 156) had higher mTMB compared to those with KRAS transitions (Ti) (n = 25) (10.9 vs 7.6 mut/Mb, P = 0.03). Median PD-L1 TPS was similar across KRAS mut alleles. Pts with KRAS G12V had longer median progression-free survival (mPFS) (5.5 vs 2.7 months, HR:0.62 [95%CI:0.40-0.96], P = 0.03) and overall survival (mOS) (17.5 vs 9.7 months, HR:0.62 [95%CI:0.36-0.99], P = 0.05), compared to non-G12V. Pts with KRAS Tv had longer mPFS and mOS compared to pts with Ti (mPFS: 3.4 vs 2.0 months, HR: 0.58 [95%CI:0.37-0.92], P = 0.02; mOS: 10.9 vs 5.4 months, HR:0.59 [95%CI:0.35-0.99], P = 0.048). Clinicopathologic features and STK11/KEAP1 mut were balanced across all KRAS mut alleles. Among KRAS mut pts, those with KEAP1 (n:52) and STK11 (n:50) concurrent mut had shorter mPFS (KEAP1 mut 1.8 vs. KEAP1 wt 4.1 months, HR: 0.55 [95%CI:0.38-0.80], P = 0.002; STK11 mut 1.8 vs STK11 wt 4.6 months, HR:0.46 [95%CI:0.32-0.67], P < 0.0001) and mOS (KEAP1 mut 4.8 vs KEAP1 wt 15.1 months, HR: 0.51 [95%CI:0.34-0.76], P = 0.001; STK11 mut 4.8 vs STK11 wt 13.6 months, HR:0.51 [95%CI:0.34-0.76], P = 0.001). KEAP1 and STK11 mut did not impact outcome in KRAS wt pts. Conclusions: KRAS allele subtypes and concurrent genomic alterations impact ICI efficacy in NSCLC.

4527Background: While the genetic characteristics of muscle-invasive bladder cancer have been studied in detail, there are few reports using the same genetic analysis platform to investigate differ...

9077 Background: DNA damage response (DDR) gene alterations are associated with increased tumor infiltrating lymphocytes, higher genomic instability, and higher tumor mutational burden (TMB) in cancer. Whether DDR alterations are associated with benefit from immune-checkpoint inhibitors (ICIs) in non-small cell lung cancer (NSCLC) is unknown. Methods: Clinicopathologic and genomic data were collected from patients (pts) with advanced NSCLC at the Dana-Farber Cancer Institute treated with PD-(L)1 inhibitors. Targeted next-generation sequencing (NGS) by OncoPanel was used to determine DDR gene mutation status and TMB. DDR positive (DDRpos) cases were defined as those with pathogenic DDR alterations (per COSMIC and ClinVar databases). DDR negative (DDRneg) cases were defined as either DDR wild-type or those with non-pathogenic DDR alterations. Results: Of 468 pts with successful NGS who received ICIs, 242 (51.7%) were identified as having any DDR alteration. Of them, 74 (15.8% in the entire cohort) were defined as DDRpos with pathogenic alterations in the following genes: ATM (41.9%), MLH1/MSH2/MSH6 (18.9%), BRCA1/2 (16.2%), CHEK1/2 (9.4%), FANC genes (9.4%), BAP1 (5.4%), RAD genes (5.4%), ERCC4/6 (4.0%), POLE (2.7%), ATR (2.7%). DDRpos and DDRneg groups were balanced in terms of age, performance status, gender, histology, oncogenic driver mutation, smoking status, ICI line, baseline brain metastases. The median TMB was significantly higher in the DDRpos group compared to the DDRneg group (12.1 vs 9.8 mutations/megabase, P = 0.007). No difference in median PD-L1 tumor proportion score was observed between groups (30% vs 25%, P = 0.33). Compared to DDRneg pts (N = 394), DDRpos pts had a significantly higher objective response rate (31.1% vs 19.1%, P = 0.03), and longer median progression-free survival (4.3 vs 2.6 months, HR: 0.71 [95%CI: 0.53-0.95], P = 0.02) and overall survival (16.3 vs 9.8 months, HR: 0.63 [95%CI: 0.45-0.89], P = 0.009) with PD-(L)1 therapy. Conclusions: Pathogenic DDR alterations are frequent in NSCLC and are associated with higher TMB and improved clinical outcomes in NSCLC pts treated with PD-1 axis inhibition.